83 citations · 114 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning from Bagged Reward
Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding +3
In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative reward…
Retrosynthetic Planning with Dual Value Networks
Guoqing Liu, Di Xue, Shufang Xie +7
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design…
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
Guoqing Liu, Mengzhang Cai, Li Zhao +4
Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requirin…
You May Not Need Ratio Clipping in PPO
Mingfei Sun, Vitaly Kurin, Guoqing Liu +4
Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. R…
Return-Based Contrastive Representation Learning for Reinforcement Learning
Guoqing Liu, Chuheng Zhang, Li Zhao +5
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Junjie Li, Sotetsu Koyamada, Qiwei Ye +7
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI h…